Multimodal Correlative Analysis of Electromyographic and Dynamometric Signals for Enhanced Prosthetic Control and Signal Decoding
Session Number
2
Advisor(s)
Dr. Ashwin Mohan, PhD, IMSA
Location
A150
Discipline
Medical and Health Sciences
Start Date
15-4-2026 11:10 AM
End Date
15-4-2026 11:55 AM
Abstract
Current prosthetic technology is focused on translating biological signals into mechanical movement. The purpose of this study was to investigate the correlation between the surface EMG (sEMG) and inertial signals obtained during forearm and wrist movements. Specifically, this study was aimed at fine and coarse motor control using signal analysis and inertial sensors, using MATLAB. Ongoing research aims to improve translation of biological signals into mechanical movement using technology. However, challenges with non-invasive approaches include electrode placement, human muscle anatomy, that vary across populations. This experiment used electrophysiological and biomechanical signals to measure the signal and the physical force output, which would increase effectiveness in the development of prosthetics. The experiment involved the use of the sEMG with a differential lead placement, which was placed on muscles on the back of the forearm, and grip force, which was measured with the use of a hand-held dynamometer with eight standardized movements. Current findings indicate a direct correlation between signal amplitude measured at the forearm and the fine motor control that measures grip strength.
Multimodal Correlative Analysis of Electromyographic and Dynamometric Signals for Enhanced Prosthetic Control and Signal Decoding
A150
Current prosthetic technology is focused on translating biological signals into mechanical movement. The purpose of this study was to investigate the correlation between the surface EMG (sEMG) and inertial signals obtained during forearm and wrist movements. Specifically, this study was aimed at fine and coarse motor control using signal analysis and inertial sensors, using MATLAB. Ongoing research aims to improve translation of biological signals into mechanical movement using technology. However, challenges with non-invasive approaches include electrode placement, human muscle anatomy, that vary across populations. This experiment used electrophysiological and biomechanical signals to measure the signal and the physical force output, which would increase effectiveness in the development of prosthetics. The experiment involved the use of the sEMG with a differential lead placement, which was placed on muscles on the back of the forearm, and grip force, which was measured with the use of a hand-held dynamometer with eight standardized movements. Current findings indicate a direct correlation between signal amplitude measured at the forearm and the fine motor control that measures grip strength.